AI RESEARCH
Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers
arXiv CS.LG
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ArXi:2606.02623v1 Announce Type: cross Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural networks (PINNs) learn PDE solutions from governing equations. However, accurately capturing temporal evolution remains challenging. Recent sequence-model-based approaches parameterize time evolution using general-purpose sequence models, which capture temporal dependencies but do not explicitly encode the structured dynamics of PDE solutions.